The present invention relates to speech recognition. More specifically, the present invention relates to filled pauses, restarts, and other noise events in continuous speech recognition.
A speech recognition system receives a speech signal and attempts to decode the speech signal to identify a string of words represented by the speech signal. Conventional speech recognizers include, among other things, an acoustic model and a language model usually from training data. The acoustic model models the acoustic features of speech units (such as phonemes) based on the training data. The language model models word order as found in the training data.
When the speech signal is received for speech recognition, acoustic features are extracted from the speech signal and compared against the models in the acoustic model to identify speech units contained in the speech signal. Once words are identified, the words are compared against the language model to determine the probability that a word was spoken, given its history (or context).
Events occurring in speech, and in particular, recognition of spontaneous or continuous speech, can present considerable problems for speech recognizers. One particular problem includes processing filled pauses such as “um”, “hmm”, “er”, “uh”, etc., the type of which may vary from language to language, or to culture to culture, but nevertheless is an utterance commonly made when a person is composing or contemplating speech to be made. Problems occur when a speech recognizer misrecognizes the filled pause as a valid word due to the high signal-to-noise ratio for the utterance of the filled pause. Other environmental situations contributing to noise such as breathing, microphone noise, keyboard operation, opening and closing of doors, as well as re-starts or false starts made by the speaker in words also contribute to errors during speech recognition.
A method or system that addresses one, some or all of the foregoing problems would be beneficial and provide improved speech recognition.